Optimization of medication distribution in Mexico through a mathematical model incorporating mortality, incidence and prevalence

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Objective: To develop a mathematical model that incorporates the mortality, incidence and prevalence of Mexico’s most common diseases—ulcer, hypertension, type 2 diabetes mellitus and obesity—in order to improve the accuracy of future medication demand predictions. The model utilizes Markov chains,...

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Detalles Bibliográficos
Autores: Corral Alemán, Querit Marianna, Valles Borrego, Carlos Alan, Hernández Saldaña, Raquel Idali, Duarte Contreras, Bryan Alejandro, Pérez Ruiz, Manuel David, Enríquez Sánchez, Luis Bernardo
Formato: artículo
Fecha de Publicación:2024
Institución:Universidad de San Martín de Porres
Repositorio:Horizonte médico
Lenguaje:español
inglés
OAI Identifier:oai:horizontemedico.usmp.edu.pe:article/2547
Enlace del recurso:https://www.horizontemedico.usmp.edu.pe/index.php/horizontemed/article/view/2547
Nivel de acceso:acceso abierto
Materia:Optimización
Medicamentos
México
Cumplimiento del Tratamiento
Process Optimization
Pharmaceutical Preparations
Mexico
Patient Compliance
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network_name_str Horizonte médico
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dc.title.none.fl_str_mv Optimization of medication distribution in Mexico through a mathematical model incorporating mortality, incidence and prevalence
Optimización en la distribución de medicamentos en México mediante un modelo matemático que incluye mortalidad, incidencia y prevalencia
title Optimization of medication distribution in Mexico through a mathematical model incorporating mortality, incidence and prevalence
spellingShingle Optimization of medication distribution in Mexico through a mathematical model incorporating mortality, incidence and prevalence
Corral Alemán, Querit Marianna
Optimización
Medicamentos
México
Cumplimiento del Tratamiento
Process Optimization
Pharmaceutical Preparations
Mexico
Patient Compliance
title_short Optimization of medication distribution in Mexico through a mathematical model incorporating mortality, incidence and prevalence
title_full Optimization of medication distribution in Mexico through a mathematical model incorporating mortality, incidence and prevalence
title_fullStr Optimization of medication distribution in Mexico through a mathematical model incorporating mortality, incidence and prevalence
title_full_unstemmed Optimization of medication distribution in Mexico through a mathematical model incorporating mortality, incidence and prevalence
title_sort Optimization of medication distribution in Mexico through a mathematical model incorporating mortality, incidence and prevalence
dc.creator.none.fl_str_mv Corral Alemán, Querit Marianna
Valles Borrego, Carlos Alan
Hernández Saldaña, Raquel Idali
Duarte Contreras, Bryan Alejandro
Pérez Ruiz, Manuel David
Enríquez Sánchez, Luis Bernardo
author Corral Alemán, Querit Marianna
author_facet Corral Alemán, Querit Marianna
Valles Borrego, Carlos Alan
Hernández Saldaña, Raquel Idali
Duarte Contreras, Bryan Alejandro
Pérez Ruiz, Manuel David
Enríquez Sánchez, Luis Bernardo
author_role author
author2 Valles Borrego, Carlos Alan
Hernández Saldaña, Raquel Idali
Duarte Contreras, Bryan Alejandro
Pérez Ruiz, Manuel David
Enríquez Sánchez, Luis Bernardo
author2_role author
author
author
author
author
dc.subject.none.fl_str_mv Optimización
Medicamentos
México
Cumplimiento del Tratamiento
Process Optimization
Pharmaceutical Preparations
Mexico
Patient Compliance
topic Optimización
Medicamentos
México
Cumplimiento del Tratamiento
Process Optimization
Pharmaceutical Preparations
Mexico
Patient Compliance
description Objective: To develop a mathematical model that incorporates the mortality, incidence and prevalence of Mexico’s most common diseases—ulcer, hypertension, type 2 diabetes mellitus and obesity—in order to improve the accuracy of future medication demand predictions. The model utilizes Markov chains, Monte Carlo simulations, econometric methods and financial projections. Materials and methods: A research design was employed using a predictive mathematical model based on econometric and f inancial approaches, such as Markov chains and Monte Carlo simulations. A simulated population of 20,000 individuals was analyzed over 10 simulation cycles in Excel, where individuals transitioned between the healthy, sick and deceased states. The model included previously researched rates of mortality, incidence and prevalence. Results: Transition tables with probabilities, based on Mexico's most common diseases, were generated in Excel. The considered states included “healthy-deceased,” “healthy-sick” and “healthy-healthy.” The “sick-deceased” transition was calculated using both disease-specific and overall mortality rates. In the second disease cycle, the annual treatment costs were as follows: 285,120 pesos for ulcer, gastritis and duodenitis; 3,525,120 pesos for hypertension; 35,490 pesos for type 2 diabetes; and 752,000 pesos for obesity. An increase in the required budget for each disease was observed since no new healthy population was added during these transitions. Conclusions: Applying a mathematical model based on epidemiological data, combined with the historical method, could improve the accuracy of pharmaceutical budget allocation. Countries such as Spain, Panama and Peru use methods that combine historical adjustments with morbidity data. More accurate, up-to-date and reliable statistics are needed to optimize the government’s financial resources for health.
publishDate 2024
dc.date.none.fl_str_mv 2024-12-10
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://www.horizontemedico.usmp.edu.pe/index.php/horizontemed/article/view/2547
10.24265/horizmed.2024.v24n4.02
url https://www.horizontemedico.usmp.edu.pe/index.php/horizontemed/article/view/2547
identifier_str_mv 10.24265/horizmed.2024.v24n4.02
dc.language.none.fl_str_mv spa
eng
language spa
eng
dc.relation.none.fl_str_mv https://www.horizontemedico.usmp.edu.pe/index.php/horizontemed/article/view/2547/1939
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https://www.horizontemedico.usmp.edu.pe/index.php/horizontemed/article/view/2547/2082
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dc.rights.none.fl_str_mv Derechos de autor 2024 Horizonte Médico (Lima)
https://creativecommons.org/licenses/by/4.0
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Derechos de autor 2024 Horizonte Médico (Lima)
https://creativecommons.org/licenses/by/4.0
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dc.format.none.fl_str_mv application/pdf
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dc.publisher.none.fl_str_mv Universidad de San Martín de Porres. Facultad de Medicina Humana
publisher.none.fl_str_mv Universidad de San Martín de Porres. Facultad de Medicina Humana
dc.source.none.fl_str_mv Horizonte Médico (Lima); Vol. 24 No. 4 (2024): Octubre-Diciembre; e2547
Horizonte Médico (Lima); Vol. 24 Núm. 4 (2024): Octubre-Diciembre; e2547
Horizonte Médico (Lima); v. 24 n. 4 (2024): Octubre-Diciembre; e2547
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spelling Optimization of medication distribution in Mexico through a mathematical model incorporating mortality, incidence and prevalenceOptimización en la distribución de medicamentos en México mediante un modelo matemático que incluye mortalidad, incidencia y prevalencia Corral Alemán, Querit MariannaValles Borrego, Carlos Alan Hernández Saldaña, Raquel Idali Duarte Contreras, Bryan Alejandro Pérez Ruiz, Manuel David Enríquez Sánchez, Luis BernardoOptimizaciónMedicamentos México Cumplimiento del TratamientoProcess Optimization Pharmaceutical Preparations Mexico Patient Compliance Objective: To develop a mathematical model that incorporates the mortality, incidence and prevalence of Mexico’s most common diseases—ulcer, hypertension, type 2 diabetes mellitus and obesity—in order to improve the accuracy of future medication demand predictions. The model utilizes Markov chains, Monte Carlo simulations, econometric methods and financial projections. Materials and methods: A research design was employed using a predictive mathematical model based on econometric and f inancial approaches, such as Markov chains and Monte Carlo simulations. A simulated population of 20,000 individuals was analyzed over 10 simulation cycles in Excel, where individuals transitioned between the healthy, sick and deceased states. The model included previously researched rates of mortality, incidence and prevalence. Results: Transition tables with probabilities, based on Mexico's most common diseases, were generated in Excel. The considered states included “healthy-deceased,” “healthy-sick” and “healthy-healthy.” The “sick-deceased” transition was calculated using both disease-specific and overall mortality rates. In the second disease cycle, the annual treatment costs were as follows: 285,120 pesos for ulcer, gastritis and duodenitis; 3,525,120 pesos for hypertension; 35,490 pesos for type 2 diabetes; and 752,000 pesos for obesity. An increase in the required budget for each disease was observed since no new healthy population was added during these transitions. Conclusions: Applying a mathematical model based on epidemiological data, combined with the historical method, could improve the accuracy of pharmaceutical budget allocation. Countries such as Spain, Panama and Peru use methods that combine historical adjustments with morbidity data. More accurate, up-to-date and reliable statistics are needed to optimize the government’s financial resources for health.Objetivo: Elaborar un modelo matemático compuesto de incidencia, mortalidad y prevalencia de cada una de las enfermedades más prevalentes en México —úlcera, hipertensión arterial, diabetes mellitus tipo 2 y obesidad— para una predicción más precisa acerca de los medicamentos que se van a utilizar en años futuros. Este modelo está basado en las teorías de Markov, Montecarlo, econometría y proyección f inanciera. Materiales y métodos: Se empleó un diseño de investigación que utilizó un modelo matemático predictivo basado en modelos econométricos y financieros, como Markov y Montecarlo. Se simuló una población de 20 000 personas para llevar a cabo el análisis en Excel, donde, a través de diez ciclos de simulación, los individuos pasaban a los estados de sano, enfermo y fallecido; se incluyeron los porcentajes previamente investigados sobre incidencia, mortalidad y prevalencia. Resultados: Se utilizó Excel para crear cuadros de transición con probabilidades basadas en datos de enfermedades comunes en México. Se consideraron los estados "sano-fallecido", "sano-enfermo" y "sano-sano". La transición "enfermo-fallecido" se calculó con la mortalidad específica de la enfermedad y la mortalidad general. En el segundo ciclo de la enfermedad, se observó que el costo del tratamiento anual para úlceras, gastritis y duodenitis fue de 285 120 pesos; para hipertensión arterial, 3 525 120; para diabetes tipo 2, 35 490, y para obesidad, 752 000. Se notó un aumento del presupuesto necesario para cada enfermedad, pues no se está agregando nueva población sana en estas transiciones. Conclusiones: El uso de un modelo matemático basado en epidemiología en combinación con el método histórico podría mejorar la precisión al distribuir el presupuesto para los medicamentos. Países como España, Panamá y Perú utilizan métodos combinados de ajuste histórico con morbilidad. Se necesita contar con mejores estadísticas actualizadas y confiables para maximizar el aprovechamiento de los recursos económicos del gobierno destinados a la salud.Universidad de San Martín de Porres. Facultad de Medicina Humana2024-12-10info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdftext/xmltext/htmlapplication/pdfhttps://www.horizontemedico.usmp.edu.pe/index.php/horizontemed/article/view/254710.24265/horizmed.2024.v24n4.02Horizonte Médico (Lima); Vol. 24 No. 4 (2024): Octubre-Diciembre; e2547Horizonte Médico (Lima); Vol. 24 Núm. 4 (2024): Octubre-Diciembre; e2547Horizonte Médico (Lima); v. 24 n. 4 (2024): Octubre-Diciembre; e25472227-35301727-558Xreponame:Horizonte médicoinstname:Universidad de San Martín de Porresinstacron:USMPspaenghttps://www.horizontemedico.usmp.edu.pe/index.php/horizontemed/article/view/2547/1939https://www.horizontemedico.usmp.edu.pe/index.php/horizontemed/article/view/2547/1999https://www.horizontemedico.usmp.edu.pe/index.php/horizontemed/article/view/2547/2082https://www.horizontemedico.usmp.edu.pe/index.php/horizontemed/article/view/2547/2236Derechos de autor 2024 Horizonte Médico (Lima)https://creativecommons.org/licenses/by/4.0info:eu-repo/semantics/openAccessoai:horizontemedico.usmp.edu.pe:article/25472024-12-16T19:07:09Z
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